




"""simple docstring"""




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase				:     List[Any]					=				logging.get_logger(__name__)

lowerCAmelCase				:     List[Any]					=				{
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		"deta"
				__UpperCamelCase         =		{
				    "hidden_size": "d_model",
				    "num_attention_heads": "encoder_attention_heads",
				}





				def __init__(			self						,					_a=None						,					_a=900						,					_a=2_048						,					_a=6						,					_a=2_048						,					_a=8						,					_a=6						,					_a=1_024						,					_a=8						,					_a=0.0						,					_a=True						,					_a="relu"						,					_a=256						,					_a=0.1						,					_a=0.0						,					_a=0.0						,					_a=0.02						,					_a=1.0						,					_a=True						,					_a=False						,					_a="sine"						,					_a=5						,					_a=4						,					_a=4						,					_a=True						,					_a=300						,					_a=True						,					_a=True						,					_a=1						,					_a=5						,					_a=2						,					_a=1						,					_a=1						,					_a=5						,					_a=2						,					_a=0.1						,					_a=0.25						,					**_a						,					):





							"""simple docstring"""
							if backbone_config is None:
										logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""					)
										lowerCamelCase      =   CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]					)
							else:
										if isinstance(_a						,					_a					):
													lowerCamelCase      =   backbone_config.pop("""model_type"""					)
													lowerCamelCase      =   CONFIG_MAPPING[backbone_model_type]
													lowerCamelCase      =   config_class.from_dict(_a					)

							lowerCamelCase      =   backbone_config
							lowerCamelCase      =   num_queries
							lowerCamelCase      =   max_position_embeddings
							lowerCamelCase      =   d_model
							lowerCamelCase      =   encoder_ffn_dim
							lowerCamelCase      =   encoder_layers
							lowerCamelCase      =   encoder_attention_heads
							lowerCamelCase      =   decoder_ffn_dim
							lowerCamelCase      =   decoder_layers
							lowerCamelCase      =   decoder_attention_heads
							lowerCamelCase      =   dropout
							lowerCamelCase      =   attention_dropout
							lowerCamelCase      =   activation_dropout
							lowerCamelCase      =   activation_function
							lowerCamelCase      =   init_std
							lowerCamelCase      =   init_xavier_std
							lowerCamelCase      =   encoder_layerdrop
							lowerCamelCase      =   auxiliary_loss
							lowerCamelCase      =   position_embedding_type
							# deformable attributes
							lowerCamelCase      =   num_feature_levels
							lowerCamelCase      =   encoder_n_points
							lowerCamelCase      =   decoder_n_points
							lowerCamelCase      =   two_stage
							lowerCamelCase      =   two_stage_num_proposals
							lowerCamelCase      =   with_box_refine
							lowerCamelCase      =   assign_first_stage
							if two_stage is True and with_box_refine is False:
										raise ValueError("""If two_stage is True, with_box_refine must be True."""					)
							# Hungarian matcher
							lowerCamelCase      =   class_cost
							lowerCamelCase      =   bbox_cost
							lowerCamelCase      =   giou_cost
							# Loss coefficients
							lowerCamelCase      =   mask_loss_coefficient
							lowerCamelCase      =   dice_loss_coefficient
							lowerCamelCase      =   bbox_loss_coefficient
							lowerCamelCase      =   giou_loss_coefficient
							lowerCamelCase      =   eos_coefficient
							lowerCamelCase      =   focal_alpha
							super().__init__(is_encoder_decoder=_a						,					**_a					)





				@property
				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							return self.encoder_attention_heads





				@property
				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							return self.d_model





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   copy.deepcopy(self.__dict__					)
							lowerCamelCase      =   self.backbone_config.to_dict()
							lowerCamelCase      =   self.__class__.model_type
							return output









"""simple docstring"""




from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map



def        a__					(							snake_case__							)		->   Dict[str, torch.Tensor]:
			lowerCamelCase      =   []
			lowerCamelCase      =   []
			lowerCamelCase      =   []

			for rt in rc.restypes:
						lowerCamelCase      =   rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
						restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]							)
						lowerCamelCase      =   {name: i for i, name in enumerate(snake_case__							)}
						restype_atomaa_to_atomaa_list.append(
						    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]							)

						restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]							)

			# Add dummy mapping for restype 'UNK'
			restype_atomaa_to_atomaa_list.append([0] * 14							)
			restype_atomaa_to_atomaa_list.append([0] * 37							)
			restype_atomaa_mask_list.append([0.0] * 14							)

			lowerCamelCase      =   torch.tensor(
			    snake_case__			,					dtype=torch.intaa			,					device=protein["""aatype"""].device			,					)
			lowerCamelCase      =   torch.tensor(
			    snake_case__			,					dtype=torch.intaa			,					device=protein["""aatype"""].device			,					)
			lowerCamelCase      =   torch.tensor(
			    snake_case__			,					dtype=torch.floataa			,					device=protein["""aatype"""].device			,					)
			lowerCamelCase      =   protein["""aatype"""].to(torch.long							)

			# create the mapping for (residx, atom14) --> atom37, i.e. an array
			# with shape (num_res, 14) containing the atom37 indices for this protein
			lowerCamelCase      =   restype_atomaa_to_atomaa[protein_aatype]
			lowerCamelCase      =   restype_atomaa_mask[protein_aatype]

			lowerCamelCase      =   residx_atomaa_mask
			lowerCamelCase      =   residx_atomaa_to_atomaa.long()

			# create the gather indices for mapping back
			lowerCamelCase      =   restype_atomaa_to_atomaa[protein_aatype]
			lowerCamelCase      =   residx_atomaa_to_atomaa.long()

			# create the corresponding mask
			lowerCamelCase      =   torch.zeros([21, 37]			,					dtype=torch.floataa			,					device=protein["""aatype"""].device							)
			for restype, restype_letter in enumerate(rc.restypes							):
						lowerCamelCase      =   rc.restype_atoa[restype_letter]
						lowerCamelCase      =   rc.residue_atoms[restype_name]
						for atom_name in atom_names:
									lowerCamelCase      =   rc.atom_order[atom_name]
									lowerCamelCase      =   1

			lowerCamelCase      =   restype_atomaa_mask[protein_aatype]
			lowerCamelCase      =   residx_atomaa_mask

			return protein



def        a__					(							snake_case__							)		->   Dict[str, np.ndarray]:
			lowerCamelCase      =   tree_map(lambda snake_case__							: torch.tensor(snake_case__			,					device=batch["""aatype"""].device							)			,					snake_case__			,					np.ndarray							)
			lowerCamelCase      =   tensor_tree_map(lambda snake_case__							: np.array(snake_case__							)			,					make_atomaa_masks(snake_case__							)							)
			return out



